Yong Xu, Qiuqiang Kong, Wenwu Wang and Mark Plumbley won the 1st prize in Task 4, ‘large-scale weakly supervised sound event detection for smart cars’, Subtask A, ‘audio tagging’ in the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2017). The DCASE challenge constitutes the most important challenge in the non-speech audio domain. It is organised by Tampere University of Technology, Carnegie Mellon University and INRIA and sponsored by Google and Audio Analytic. Because of its unique standing, the best players in the field participate such as CMU, New York University, Bosch, USC, TUT, Singapore A*Star, Korean Advanced Institute of Science and Technology, Seoul National University, National Taiwan University and CVSSP.

In this report we describe the creation of a random, data-blind system to provide a random baseline for Task 3 (sound event detection in real life audio) in the DCASE 2016 challenge. Particular attention is paid to the results of two sound events occurring in the residential area scene, one very rare, the other very frequent. The relatively good performance of the random system in comparison to the results of the proper detection systems shows the difficulty of Task 3 given the current state-of-the-art sound detection methods.